A Broad Learning Approach for Context-Aware Mobile Application Recommendation

TL;DR

This paper introduces a tensor-based, context-aware recommendation method for mobile apps that integrates user preferences, app categories, and multi-view features to improve rating predictions.

Contribution

It proposes a novel tensor framework with Tucker decomposition and group regularization to model complex interactions in app recommendation tasks.

Findings

Outperforms existing methods on real-world datasets.

Effectively captures multi-view and category interactions.

Improves accuracy of app rating predictions.

Abstract

With the rapid development of mobile apps, the availability of a large number of mobile apps in application stores brings challenge to locate appropriate apps for users. Providing accurate mobile app recommendation for users becomes an imperative task. Conventional approaches mainly focus on learning users' preferences and app features to predict the user-app ratings. However, most of them did not consider the interactions among the context information of apps. To address this issue, we propose a broad learning approach for \textbf{C}ontext-\textbf{A}ware app recommendation with \textbf{T}ensor \textbf{A}nalysis (CATA). Specifically, we utilize a tensor-based framework to effectively integrate user's preference, app category information and multi-view features to facilitate the performance of app rating prediction. The multidimensional structure is employed to capture the hidden relationships between multiple app categories with multi-view features. We develop an efficient factorization method which applies Tucker decomposition to learn the full-order interactions within multiple categories and features. Furthermore, we employ a group $\ell_{1}-$norm regularization to learn the group-wise feature importance of each view with respect to each app category. Experiments on two real-world mobile app datasets demonstrate the effectiveness of the proposed method.